# import numpy as np 
# import matplotlib.pyplot as plt 
  
# # let img1 be an image with no features 
# img1 = np.array([np.array([200, 200]), np.array([200, 200])]) 
# img2 = np.array([np.array([200, 200]), np.array([0, 0])]) 
# img3 = np.array([np.array([200, 0]), np.array([200, 0])]) 
  
# kernel_horizontal = np.array([np.array([2, 2]), np.array([-2, -2])]) 
# print(kernel_horizontal, 'is a kernel for detecting horizontal edges') 
  
# kernel_vertical = np.array([np.array([2, -2]), np.array([2, -2])]) 
# print(kernel_vertical, 'is a kernel for detecting vertical edges') 
  
# # We will apply the kernels on the images by 
# # elementwise multiplication followed by summation 
# def apply_kernel(img, kernel): 
#     return np.sum(np.multiply(img, kernel)) 
  
# # Visualizing img1 
# plt.imshow(img1) 
# plt.axis('off') 
# plt.title('img1') 
# plt.show() 
  
# # Checking for horizontal and vertical features in image1 
# print('Horizontal edge confidence score:', apply_kernel(img1,  
#                                             kernel_horizontal)) 
# print('Vertical edge confidence score:', apply_kernel(img1,  
#                                             kernel_vertical)) 
  
# # Visualizing img2 
# plt.imshow(img2) 
# plt.axis('off') 
# plt.title('img2') 
# plt.show() 
  
# # Checking for horizontal and vertical features in image2 
# print('Horizontal edge confidence score:', apply_kernel(img2,  
#                                             kernel_horizontal)) 
# print('Vertical edge confidence score:', apply_kernel(img2,  
#                                             kernel_vertical)) 
  
# # Visualizing img3 
# plt.imshow(img3) 
# plt.axis('off') 
# plt.title('img3') 
# plt.show() 
  
# # Checking for horizontal and vertical features in image3 
# print('Horizontal edge confidence score:', apply_kernel(img3,  
#                                             kernel_horizontal)) 
# print('Vertical edge confidence score:', apply_kernel(img3,  
#                                             kernel_vertical)) 
